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Found 23 papers, 2 papers with code
bnRep: A repository of Bayesian networks from the academic literature
Bayesian networks (BNs) are widely used for modeling complex systems with uncertainty, yet repositories of pre-built BNs remain limited.
The diameter of a stochastic matrix: A new measure for sensitivity analysis in Bayesian networks
Bayesian networks are one of the most widely used classes of probabilistic models for risk management and decision support because of their interpretability and flexibility in including heterogeneous pieces of information.
Global Sensitivity Analysis of Uncertain Parameters in Bayesian Networks
Last, we apply the method of Sobol to the resulting network to obtain $n$ global sensitivity indices.
Context-Specific Refinements of Bayesian Network Classifiers
Generative classifiers based on Bayesian networks are often used because of their interpretability and competitive accuracy.
Learning Staged Trees from Incomplete Data
Staged trees are probabilistic graphical models capable of representing any class of non-symmetric independence via a coloring of its vertices.
AI and the creative realm: A short review of current and future applications
This study explores the concept of creativity and artificial intelligence (AI) and their recent integration.
The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networks
Sensitivity analysis measures the influence of a Bayesian network's parameters on a quantity of interest defined by the network, such as the probability of a variable taking a specific value.
Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear
Bayesian networks are widely used to learn and reason about the dependence structure of discrete variables.
You Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks
Sensitivity analysis measures the influence of a Bayesian network's parameters on a quantity of interest defined by the network, such as the probability of a variable taking a specific value.
Highly Efficient Structural Learning of Sparse Staged Trees
Several structural learning algorithms for staged tree models, an asymmetric extension of Bayesian networks, have been defined.
Structural Learning of Simple Staged Trees
Bayesian networks faithfully represent the symmetric conditional independences existing between the components of a random vector.
Global sensitivity analysis in probabilistic graphical models
We show how to apply Sobol's method of global sensitivity analysis to measure the influence exerted by a set of nodes' evidence on a quantity of interest expressed by a Bayesian network.
Staged trees and asymmetry-labeled DAGs
Bayesian networks are a widely-used class of probabilistic graphical models capable of representing symmetric conditional independence between variables of interest using the topology of the underlying graph.
Sensitivity and robustness analysis in Bayesian networks with the bnmonitor R package
Bayesian networks are a class of models that are widely used for risk assessment of complex operational systems.
Context-Specific Causal Discovery for Categorical Data Using Staged Trees
Causal discovery algorithms aim at untangling complex causal relationships from data.
A new class of generative classifiers based on staged tree models
Generative models for classification use the joint probability distribution of the class variable and the features to construct a decision rule.
The curved exponential family of a staged tree
Staged tree models are a discrete generalization of Bayesian networks.
Statistics Theory Methodology Statistics Theory
The R Package stagedtrees for Structural Learning of Stratified Staged Trees
stagedtrees is an R package which includes several algorithms for learning the structure of staged trees and chain event graphs from data.
A geometric characterisation of sensitivity analysis in monomial models
Sensitivity analysis in probabilistic discrete graphical models is usually conducted by varying one probability value at a time and observing how this affects output probabilities of interest.
Model-Preserving Sensitivity Analysis for Families of Gaussian Distributions
However, for Gaussian graphical models, such variations usually make the original graph an incoherent representation of the model's conditional independence structure.
Directed expected utility networks
We then proceed with the construction of a directed expected utility network to support decision makers in the domain of household food security.
A symbolic algebra for the computation of expected utilities in multiplicative influence diagrams
For problems where all random variables and decision spaces are finite and discrete, here we develop a symbolic way to calculate the expected utilities of influence diagrams that does not require a full numerical representation.
Sensitivity analysis, multilinearity and beyond
Sensitivity methods for the analysis of the outputs of discrete Bayesian networks have been extensively studied and implemented in different software packages.
